feat: enforce full AI outbound generation and reduce template replies

core/ai_reply_flow.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Optional, Tuple
 from core.post_ops import negotiation_strategy_reply
 
 logger = logging.getLogger("cs_agent")
@@ -10,7 +10,43 @@ if TYPE_CHECKING:
     from core.pydantic_ai_agent import AgentDeps, ConversationState, CustomerMessage, CustomerServiceAgent
 
 
-def select_target_agent(agent: "CustomerServiceAgent", message: "CustomerMessage", state: "ConversationState"):
+def _select_agent_by_intent(
+    agent: "CustomerServiceAgent",
+    message: "CustomerMessage",
+    state: "ConversationState",
+) -> Tuple[Optional[Any], str]:
+    """
+    AI 意图优先路由；识别不到时返回 (None, "intent:none")，由关键词兜底。
+    """
+    try:
+        from utils.intent_analyzer import detect_intent
+
+        decision = detect_intent(message.msg or "")
+        intent = (decision.intent or "").strip()
+        source = decision.source or "none"
+        score = float(decision.score or 0.0)
+    except Exception:
+        intent, source, score = "", "error", 0.0
+
+    if not intent:
+        return None, "intent:none"
+
+    if intent in ("询价", "砍价"):
+        return agent.agent_pricing, f"intent:{intent}|src:{source}|score:{score:.3f}"
+    if intent in ("修改", "加急"):
+        return agent.agent_processing, f"intent:{intent}|src:{source}|score:{score:.3f}"
+    if intent == "售后":
+        return agent.agent_after_sale, f"intent:{intent}|src:{source}|score:{score:.3f}"
+    if intent == "转接":
+        return agent.agent_after_sale, f"intent:{intent}|src:{source}|score:{score:.3f}"
+    if intent in ("打招呼", "批量", "发图"):
+        target = agent.agent_after_sale if state.stage == "售后" else agent.agent
+        return target, f"intent:{intent}|src:{source}|score:{score:.3f}"
+
+    return None, f"intent:unmapped:{intent}|src:{source}|score:{score:.3f}"
+
+
+def select_target_agent(agent: "CustomerServiceAgent", message: "CustomerMessage", state: "ConversationState") -> Tuple[Any, str]:
     msg_lower = message.msg.lower()
     pricing_kw = ["多少钱", "多少一张", "报价", "给个价", "几块", "价位", "能便宜点吗"]
     processing_kw = ["安排", "处理一下", "开始做", "做一下", "尽快", "加急", "付款了", "已付款"]
@@ -45,18 +81,23 @@ def select_target_agent(agent: "CustomerServiceAgent", message: "CustomerMessage
         "卫星地图",
     ]
     target_agent = agent.agent_after_sale if state.stage == "售后" else agent.agent
+
+    ai_target, ai_reason = _select_agent_by_intent(agent, message, state)
+    if ai_target is not None:
+        return ai_target, ai_reason
+
     risk_hit = any(k in msg_lower for k in risk_kw) or agent._is_political_inquiry(message.msg) or agent._is_map_inquiry(message.msg)
     if risk_hit:
-        return agent.agent_risk
+        return agent.agent_risk, "keyword:risk"
     if any(k in message.msg for k in order_markers):
-        return agent.agent_order
+        return agent.agent_order, "keyword:order"
     if any(k in msg_lower for k in processing_kw):
-        return agent.agent_processing
+        return agent.agent_processing, "keyword:processing"
     if any(k in msg_lower for k in pricing_kw):
-        return agent.agent_pricing
+        return agent.agent_pricing, "keyword:pricing"
     if any(k in msg_lower for k in similar_kw):
-        return agent.agent_similar
-    return target_agent
+        return agent.agent_similar, "keyword:similar"
+    return target_agent, "fallback:default"
 
 
 async def execute_ai_turn(
@@ -68,7 +109,8 @@ async def execute_ai_turn(
     deps: "AgentDeps",
     history: list,
 ) -> str:
-    target_agent = select_target_agent(agent, message, state)
+    target_agent, route_reason = select_target_agent(agent, message, state)
+    logger.info("[路由] %s", route_reason)
     result = await target_agent.run(user_prompt, deps=deps, message_history=history)
     agent.message_histories[message.from_id] = result.all_messages()[-30:]
     reply_text = agent._colloquialize_reply(agent._normalize_reply_text(result.output))